def create_policy(self, serving: bool) -> Policy:
"""Create an online DiscreteDQN Policy from env."""
if serving:
return create_predictor_policy_from_model(
self.build_serving_module(), rl_parameters=self.rl_parameters)
else:
sampler = GreedyActionSampler()
# pyre-fixme[16]: `RLTrainer` has no attribute `q_network`.
scorer = discrete_dqn_scorer(self.trainer.q_network)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(self) -> Policy:
""" Create an online DiscreteDQN Policy from env.
"""
# Avoiding potentially importing gym when it's not installed
from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
from reagent.gym.policies.scorers.discrete_scorer import discrete_dqn_scorer
sampler = SoftmaxActionSampler(temperature=self.rl_parameters.temperature)
scorer = discrete_dqn_scorer(self.trainer.q_network)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(self, serving: bool) -> Policy:
""" Create an online DiscreteDQN Policy from env. """
if serving:
sampler = GreedyActionSampler()
scorer = discrete_dqn_serving_scorer(
DiscreteDqnPredictorUnwrapper(self.build_serving_module()))
else:
# pyre-fixme[16]: `DiscreteDQNBase` has no attribute `rl_parameters`.
# pyre-fixme[16]: `DiscreteDQNBase` has no attribute `rl_parameters`.
# pyre-fixme[16]: `DiscreteDQNBase` has no attribute `rl_parameters`.
sampler = SoftmaxActionSampler(
temperature=self.rl_parameters.temperature)
# pyre-fixme[16]: `RLTrainer` has no attribute `q_network`.
scorer = discrete_dqn_scorer(self.trainer.q_network)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(self, serving: bool) -> Policy:
""" Create an online DiscreteDQN Policy from env. """
from reagent.gym.policies.samplers.discrete_sampler import SoftmaxActionSampler
from reagent.gym.policies.scorers.discrete_scorer import (
discrete_dqn_scorer,
discrete_dqn_serving_scorer,
)
sampler = SoftmaxActionSampler(temperature=self.rl_parameters.temperature)
if serving:
scorer = discrete_dqn_serving_scorer(
DiscreteDqnPredictorUnwrapper(self.build_serving_module())
)
else:
scorer = discrete_dqn_scorer(self.trainer.q_network)
return Policy(scorer=scorer, sampler=sampler)
def create_policy(
self,
trainer_module: ReAgentLightningModule,
serving: bool = False,
normalization_data_map: Optional[Dict[str, NormalizationData]] = None,
) -> Policy:
"""Create an online DiscreteDQN Policy from env."""
if serving:
assert normalization_data_map
return create_predictor_policy_from_model(
self.build_serving_module(trainer_module, normalization_data_map),
rl_parameters=self.rl_parameters,
)
else:
sampler = GreedyActionSampler()
# pyre-fixme[6]: Expected `ModelBase` for 1st param but got
# `Union[torch.Tensor, torch.nn.Module]`.
scorer = discrete_dqn_scorer(trainer_module.q_network)
return Policy(scorer=scorer, sampler=sampler)
